Secure multi-party computation approaches help create methods for parties to jointly process their data while keeping their respective data private from one another. Stated differently, these approaches allow multiple parties to jointly compute value(s) based on individually held secret pieces of information without revealing their respective confidential information to one another in the process.
This is useful, for example, when users, such as companies and firms, need to communicate and exchange ideas but need to keep their underlying data secured. For example, merchant data owners may be interested in pooling data together to perform transactional data analysis. The merchant data owners, in this example, can view a summarized version of the transactional data (or another form of aggregated data) and not the underlying data.
While secure multi-party computation approaches help generate such summarized data without surfacing the underlying confidential data of each party to others, today's approaches for securing the data exchange between parties, however, tend to be slow from a performance perspective.